Automation of Vehicle Identification at Night using Light source recognition

Humnabadkar, Aditya; Kulkarni, Omkar N.; Rajani, P. K.

doi:10.1109/iccubea47591.2019.9128761

Cited by 3 publications

(1 citation statement)

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“…We can also use this data to check our accuracy. [5] Feature scaling method for uniformizing the independent characteristics that are present in the data within a given range. For accurate prediction, this technique is essential.…”

Section: Methodsmentioning

confidence: 99%

Comparison of Classification Algorithm for Crop Decision based on Environmental Factors using Machine Learning

Deshpande,

P. K.,

Khandagle

et al. 2023

IJRITCC

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Crop decision is a very complex process. In Agriculture it plays a vital role. Various biotic and abiotic factors affect this decision. Some crucial Environmental factors are Nitrogen Phosphorus, Potassium, pH, Temperature, Humidity, Rainfall. Machine Learning Algorithm can perfectly predict the crop necessary for this environmental condition. Various algorithms and model are used for this process such as feature selection, data cleaning, Training, and testing split etc. Algorithms such as Logistic regression, Decision Tree, Support vector machine, K- Nearest Neighbour, Navies Bayes, Random Forest. A comparison based on the accuracy parameter is presented in this paper along with various training and testing split for optimal choice of best algorithm. This comparison is done on two tools i.e., on Google collab using python and its libraries for implementation of Machine Learning Algorithm and WEKA which is a pre-processing tool to compare various algorithm of machine learning.

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Section: Methodsmentioning

confidence: 99%